Gonorrhea is the second most common, notifiable disease in the United States. In 2005, gonorrhea rates began to rise, highlighting failure of current control efforts. The broad aim of the proposed research is to use molecular evolutionary analyses to better understand the epidemiology of gonorrhea and to help in the design of infection control measures. Control of gonorrhea requires identification of individuals at high risk for transmission of N. gonorrhoeae. The core theory is a guiding principle for identification of high risk transmitters. However, implementation of control programs based on the core concept has been hampered by the lack of a precise, objective definition of the core group. We propose a novel approach to define the core;one based on the genetic characteristics of the strains circulating within the core (aim 1). The emergence of antibiotic resistance has repeatedly been a challenge for control of gonorrhea. In this proposal we will expand on preliminary studies that showed differences in genetic population structure of N. gonorrhoeae in communities with different prevalences of quinolone resistant gonococci (aim 2.1). We will also characterize population genetics of cephalosporin resistance-associated genes in Shanghai, a community where resistance is likely to emerge in the coming years (aim 2.2). Most methods used to quantify population dynamics and estimate population genetic parameters consider one or a few descriptors and make simplifying assumptions about the rest. In aim 3, we propose to assess the performance of methods to infer population dynamics and estimate population genetic parameters using simulated populations of known ancestry with different levels of recombination, natural selection, population subdivision, population sizes, substitution rates, models of evolution, and sampling strategies. PUBLIC HEALTH RELEVANCE: The control of gonorrhea has traditionally been impeded by difficulty in identifying high- risk transmitters and the emergence of antibiotic resistance. This application will explore use of genetic data and population genetic analyses to identify groups of high transmitters (core groups) and to understand and possibly predict the spread of antibiotic resistant gonococci. Finally, the research will address important issues affecting the performance of methods used to define evolutionary relationships among bacterial strains.